College of Public Administration, Huazhong University of Science and Technology, No 1037 Luau Road, Hongshan District, Wuhan, 430074, Hubei, China.
Non-traditional Security Institute, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China.
BMC Public Health. 2021 Nov 4;21(1):2001. doi: 10.1186/s12889-021-12065-0.
As COVID-19 continues to spread globally, traditional emergency management measures are facing many practical limitations. The application of big data analysis technology provides an opportunity for local governments to conduct the COVID-19 epidemic emergency management more scientifically. The present study, based on emergency management lifecycle theory, includes a comprehensive analysis of the application framework of China's SARS epidemic emergency management lacked the support of big data technology in 2003. In contrast, this study first proposes a more agile and efficient application framework, supported by big data technology, for the COVID-19 epidemic emergency management and then analyses the differences between the two frameworks.
This study takes Hainan Province, China as its case study by using a file content analysis and semistructured interviews to systematically comprehend the strategy and mechanism of Hainan's application of big data technology in its COVID-19 epidemic emergency management.
Hainan Province adopted big data technology during the four stages, i.e., migration, preparedness, response, and recovery, of its COVID-19 epidemic emergency management. Hainan Province developed advanced big data management mechanisms and technologies for practical epidemic emergency management, thereby verifying the feasibility and value of the big data technology application framework we propose.
This study provides empirical evidence for certain aspects of the theory, mechanism, and technology for local governments in different countries and regions to apply, in a precise, agile, and evidence-based manner, big data technology in their formulations of comprehensive COVID-19 epidemic emergency management strategies.
随着 COVID-19 在全球范围内的持续传播，传统的应急管理措施正面临着许多实际限制。大数据分析技术的应用为地方政府提供了一个更科学地进行 COVID-19 疫情应急管理的机会。本研究基于应急管理生命周期理论，综合分析了 2003 年中国 SARS 疫情应急管理缺乏大数据技术支持的应用框架。相比之下，本研究首先提出了一个更灵活高效的应用框架，该框架由大数据技术支持，用于 COVID-19 疫情应急管理，然后分析了两个框架之间的差异。
本研究以中国海南省为例，通过文件内容分析和半结构化访谈，系统地理解了海南省在 COVID-19 疫情应急管理中应用大数据技术的策略和机制。
海南省在 COVID-19 疫情应急管理的迁移、准备、应对和恢复四个阶段都采用了大数据技术。海南省为实际的疫情应急管理开发了先进的大数据管理机制和技术，从而验证了我们提出的大数据技术应用框架的可行性和价值。
本研究为不同国家和地区的地方政府在制定全面的 COVID-19 疫情应急管理策略时，以精确、灵活和基于证据的方式应用大数据技术提供了理论、机制和技术方面的实证证据。
